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The convective micropolar fluid flow over a permeable shrinking sheet in the presence of a heat source and thermal radiation with the magnetic field directed towards the sheet has been studied in this paper. The mathematical formulation considers the partial slip condition at the sheet, allowing a realistic representation of the fluid flow near the boundary. The governing equations for the flow, heat, and mass transfer are formulated using the conservation laws of mass, momentum, angular momentum, energy, and concentration. The resulting nonlinear partial differential equations are transformed into a system of ordinary differential equations using suitable similarity transformations. The numerical solutions are obtained using robust computational techniques to examine the influence of various parameters on the velocity, temperature, and concentration profiles. The impact of slip effects, micropolar fluid characteristics, and permeability parameters on the flow features and heat transfer rates are thoroughly analyzed. The findings of this investigation offer valuable insights into the behavior of micropolar fluids in free convection flows over permeable shrinking sheets with slip, providing a foundation for potential applications in various industrial and engineering processes. Key findings include the observation that the velocity profile overshoots for assisting flow with decreasing viscous force and rising magnetic effects as opposed to opposing flow. The thermal boundary layer thickness decreases due to buoyant force but shows increasing behavior with heat source parameters. The present result agrees with the earlier findings for specific parameter values in particular cases. [ABSTRACT FROM AUTHOR]

Heat shock proteins (HSPs) are developmentally conserved families of protein found in both prokaryotic and eukaryotic organisms. HSPs are engaged in a diverse range of physiological processes, including molecular chaperone activity to assist the initial protein folding or promote the unfolding and refolding of misfolded intermediates to acquire the normal or native conformation and its translocation and prevent protein aggregation as well as in immunity, apoptosis, and autophagy. These molecular chaperonins are classified into various families according to their molecular size or weight, encompassing small HSPs (e.g., HSP10 and HSP27), HSP40, HSP60, HSP70, HSP90, and the category of large HSPs that include HSP100 and ClpB proteins. The overexpression of HSPs is induced to counteract cell stress at elevated levels in a variety of solid tumors, including anticancer chemotherapy, and is closely related to a worse prognosis and therapeutic resistance to cancer cells. HSPs are also involved in anti-apoptotic properties and are associated with processes of cancer progression and development, such as metastasis, invasion, and cell proliferation. This review outlines the previously mentioned HSPs and their significant involvement in diverse mechanisms of tumor advancement and metastasis, as well as their contribution to identifying potential targets for therapeutic interventions.

Cancer is one of the most dangerous health problems in the millennium and it is the third foremost human cause of death in the universe. Traditional cancer treatments face several disadvantages and cannot often afford adequate outcomes. It has been exhibited that the outcome of several therapies can be improved when associated with nanostructures. In addition, a modern tendency is being developed in cancer therapy to convert single-modal into multi-modal therapies with the help of existing various nanostructures. Among them, gold is the most successful nanostructure for biomedical applications due to its flexibility in preparation, stabilization, surface modifications, less cytotoxicity, and ease of bio-detection. In the past few decades, gold-based nanomaterials rule cancer treatment applications, currently, gold nanostructures were the leading nanomaterials for synergetic cancer therapies. In this review article, the synthesis, stabilization, and optical properties of gold nanostructures have been discussed. Then, the surface modifications and targeting mechanisms of gold nanomaterials will be described. Recent signs of progress in the application of gold nanomaterials for synergetic cancer therapies such as photodynamic and photo-thermal therapies in combination with other common interventions such as radiotherapy, chemotherapy, and will be reviewed. Also, a summary of the pharmacokinetics of gold nanostructures will be delivered. Finally, the challenges and outlooks of the gold nanostructures in the clinics for applications in cancer treatments are debated.

Finding a vaccine or specific antiviral treatment for a global pandemic of virus diseases (such as the ongoing COVID-19) requires rapid analysis, annotation and evaluation of metagenomic libraries to enable a quick and efficient screening of nucleotide sequences. Traditional sequence alignment methods are not suitable and there is a need for fast alignment-free techniques for sequence analysis. Information theory and data compression algorithms provide a rich set of mathematical and computational tools to capture essential patterns in biological sequences. In this study, we investigate the use of compression-complexity (Effort-to-Compress or ETC and Lempel-Ziv or LZ complexity) based distance measures for analyzing genomic sequences. The proposed distance measure is used to successfully reproduce the phylogenetic trees for a mammalian dataset consisting of eight species clusters, a set of coronaviruses belonging to group I, group II, group III, and SARS-CoV-1 coronaviruses, and a set of coronaviruses causing COVID-19 (SARS-CoV-2), and those not causing COVID-19. Having demonstrated the usefulness of these compression complexity measures, we employ them for the automatic classification of COVID-19-causing genome sequences using machine learning techniques. Two flavors of SVM (linear and quadratic) along with linear discriminant and fine K Nearest Neighbors classifer are used for classification. Using a data set comprising 1001 coronavirus sequences (causing COVID-19 and those not causing COVID-19), a classification accuracy of 98% is achieved with a sensitivity of 95% and a specificity of 99.8%. This work could be extended further to enable medical practitioners to automatically identify and characterize coronavirus strains and their rapidly growing mutants in a fast and efficient fashion.

The potentiality of nanomedicine in the cancer treatment being widely recognized in the recent years. In the present investigation, the synergistic effects of chitosan-modified selenium nanoparticles loaded with paclitaxel (PTX-chit-SeNPs) were studied. These selenium nanoparticles were tested for drug release analysis at a pH of 7.4 and 5.5, and further characterized using FTIR, DLS, zeta potential, and TEM to confirm their morphology, and the encapsulation of the drug was carried out using UPLC analysis. Quantitative evaluation of anti-cancer properties was performed via MTT analysis, apoptosis, gene expression analysis, cell cycle arrest, and over-production of ROS. The unique combination of phytochemicals from the seed extract, chitosan, paclitaxel, and selenium nanoparticles can be effectively utilized to combat cancerous cells. The production of the nanosystem has been demonstrated to be cost-effective and have unique characteristics, and can be utilized for improving future diagnostic approaches.

Carbon nanodots (CNDs) are advanced nanomaterials with a size of 2-10 nm and are considered zero-dimensional carbonaceous materials. CNDs have received great attention in the area of cancer theranostics. The majority of review articles have shown the improvement of CNDs for use in cancer therapy and bioimaging applications. However, there is a minimal number of consolidated studies on the currently developed doped CNDs that are used in various ways in cancer therapies. Hence, in this review, we discuss the current developments in different types of heteroatom elements/metal ion-doped CNDs along with their preparations, physicochemical and biological properties, multimodal-imaging, and emerging applications in image-guided photodynamic therapies for cancer.

In today's scenario, composite materials play a vital role in automobile, aerospace, and defence sectors because of their higher strength, light weight and other mechanical properties. Aluminium alloy Al6082 is a medium strength alloy with good corrosion resistance properties; hence, it is used for high-stress applications, bridges, cranes, etc. The present work focuses on comparing the mechanical properties of Al6082 and Al6082 with the addition of silicon nitride, magnesium, and bio waste of eggshells. Samples of Al6082 reinforced with 2% of silicon nitride (Si 3 N 4 ), 5% of eggshell, and 1% magnesium reinforcements were prepared using the crucible casting process. Mechanical properties were evaluated through hardness test, tensile test and compressive tests, which varied with the additives of reinforcement materials. The results showed that the reinforced materials could increase mechanical properties. Further, it will be analysed by the machining parameters involved through the CNC turning process. Analysis of variance from optimisation technique shows a noteworthy increment of material removal rate (MRR) and significant decrement in surface roughness (Ra) and machining time compared to standard aluminium. Additionally, the analysis of mechanical testing has been predicted with the outcomes of stresses and displacements using the ANSYS platform.

In this paper, we propose a novel squeeze M-SegNet (SM-SegNet) architecture featuring a fire module to perform accurate as well as fast segmentation of the brain on magnetic resonance imaging (MRI) scans. The proposed model utilizes uniform input patches, combined-connections, long skip connections, and squeeze-expand convolutional layers from the fire module to segment brain MRI data. The proposed SM-SegNet architecture involves a multi-scale deep network on the encoder side and deep supervision on the decoder side, which uses combined-connections (skip connections and pooling indices) from the encoder to the decoder layer. The multi-scale side input layers support the deep network layers' extraction of discriminative feature information, and the decoder side provides deep supervision to reduce the gradient problem. By using combined-connections, extracted features can be transferred from the encoder to the decoder resulting in recovering spatial information, which makes the model converge faster. Long skip connections were used to stabilize the gradient updates in the network. Owing to the adoption of the fire module, the proposed model was significantly faster to train and offered a more efficient memory usage with 83% fewer parameters than previously developed methods, owing to the adoption of the fire module. The proposed method was evaluated using the open-access series of imaging studies (OASIS) and the internet brain segmentation registry (IBSR) datasets. The experimental results demonstrate that the proposed SM-SegNet architecture achieves segmentation accuracies of 95% for cerebrospinal fluid, 95% for gray matter, and 96% for white matter, which outperforms the existing methods in both subjective and objective metrics in brain MRI segmentation.

We propose an encoder-decoder architecture using wide and deep convolutional layers combined with different aggregation modules for the segmentation of medical images. Initially, we obtain a rich representation of features that span from low to high levels and from small to large scales by stacking multiple k × k kernels, where each k × k kernel operation is split into k × 1 and 1 × k convolutions. In addition, we introduce two feature-aggregation modules-multiscale feature aggregation ( MFA ) and hierarchical feature aggregation ( HFA )-to better fuse information across end-to-end network layers. The MFA module progressively aggregates features and enriches feature representation, whereas the HFA module merges the features iteratively and hierarchically to learn richer combinations of the feature hierarchy. Furthermore, because residual connections are advantageous for assembling very deep networks, we employ an MFA -based long residual connections to avoid vanishing gradients along the aggregation paths. In addition, a guided block with multilevel convolution provides effective attention to the features that were copied from the encoder to the decoder to recover spatial information. Thus, the proposed method using feature-aggregation modules combined with a guided skip connection improves the segmentation accuracy, achieving a high similarity index for ground-truth segmentation maps. Experimental results indicate that the proposed model achieves a superior segmentation performance to that obtained by conventional methods for skin-lesion segmentation, with an average accuracy score of 0.97 on the ISIC-2018, PH2, and UFBA-UESC datasets.

The object recognition concept is being widely used a result of increasing CCTV surveillance and the need for automatic object or activity detection from images or video. Increases in the use of various sensor networks have also raised the need of lightweight process frameworks. Much research has been carried out in this area, but the research scope is colossal as it deals with open-ended problems such as being able to achieve high accuracy in little time using lightweight process frameworks. Convolution Neural Networks and their variants are widely used in various computer vision activities, but most of the architectures of CNN are application-specific. There is always a need for generic architectures with better performance. This paper introduces the Dimension-Based Generic Convolution Block (DBGC), which can be used with any CNN to make the architecture generic and provide a dimension-wise selection of various height, width, and depth kernels. This single unit which uses the separable convolution concept provides multiple combinations using various dimension-based kernels. This single unit can be used for height-based, width-based, or depth-based dimensions; the same unit can even be used for height and width, width and depth, and depth and height dimensions. It can also be used for combinations involving all three dimensions of height, width, and depth. The main novelty of DBGC lies in the dimension selector block included in the proposed architecture. Proposed unoptimized kernel dimensions reduce FLOPs by around one third and also reduce the accuracy by around one half; semi-optimized kernel dimensions yield almost the same or higher accuracy with half the FLOPs of the original architecture, while optimized kernel dimensions provide 5 to 6% higher accuracy with around a 10 M reduction in FLOPs.

AA2050-T84 alloy is widely used in primary structures of modern transport aircraft. AA2050-T84 is established as a low-density aluminum alloy with improved Young's modulus, less anisotropy, and temperature-dependent mechanical properties. During flights, loading rate and temperature variation in aircraft engine subsequent parts are commonly observed. The present work focuses on the effect of loading rate and temperature on tensile and fracture properties of the 50 mm thick (2-inch) AA2050-T84 alloy plate. Quasi-static strain rates of 0.01, 0.1, and 1 s -1 at -20 °C, 24 °C and 200 °C are considered. Tensile test results revealed the sensitivity of mechanical properties towards strain rate variations for considered temperatures. The key tensile properties, yield, and ultimate tensile stresses were positive strain rate dependent. However, Young's modulus and elongation showed negative strain rate dependency. Experimental fracture toughness tests exhibited the lower Plane Strain Fracture Toughness ( K IC ) at -20 °C compared to 24 °C. Elastic numerical fracture analysis revealed that the crack driving and constraint parameters are positive strain rate dependent and maximum at -20 °C, if plotted and analyzed over the stress ratio. The current results concerning strain rates and temperatures will help in understanding the performance-related issues of AA2050-T84 alloy reported in aircraft applications.

Distributed denial-of-service (DDoS) attacks are significant threats to the cyber world because of their potential to quickly bring down victims. Memcached vulnerabilities have been targeted by attackers using DDoS amplification attacks. GitHub and Arbor Networks were the victims of Memcached DDoS attacks with 1.3 Tbps and 1.8 Tbps attack strengths, respectively. The bandwidth amplification factor of nearly 50,000 makes Memcached the deadliest DDoS attack vector to date. In recent times, fellow researchers have made specific efforts to analyze and evaluate Memcached vulnerabilities; however, the solutions provided for security are based on best practices by users and service providers. This study is the first attempt at modifying the architecture of Memcached servers in the context of improving security against DDoS attacks. This study discusses the Memcached protocol, the vulnerabilities associated with it, the future challenges for different IoT applications associated with caches, and the solutions for detecting Memcached DDoS attacks. The proposed solution is a novel identification-pattern mechanism using a threshold scheme for detecting volume-based DDoS attacks. In the undertaken study, the solution acts as a pre-emptive measure for detecting DDoS attacks while maintaining low latency and high throughput.

Polymeric nanocomposites have received significant attention in both scientific and industrial research in recent years. The demand for new methods of food preservation to ensure high-quality, healthy foods with an extended shelf life has increased. Packaging, a crucial feature of the food industry, plays a vital role in satisfying this demand. Polymeric nanocomposites exhibit remarkably improved packaging properties, including barrier properties, oxygen impermeability, solvent resistance, moisture permeability, thermal stability, and antimicrobial characteristics. Bio-based polymers have drawn considerable interest to mitigate the influence and application of petroleum-derived polymeric materials and related environmental concerns. The integration of nanotechnology in food packaging systems has shown promise for enhancing the quality and shelf life of food. This article provides a general overview of bio-based polymeric nanocomposites comprising polymer matrices and inorganic nanoparticles, and describes their classification, fabrication, properties, and applications for active food packaging systems with future perspectives.

Pancreatic ductal adenocarcinoma (PDAC) remains among the deadliest solid tumors that remain treatment-refractory and show a dismal prognosis. More than 90% of PDAC tumors harbor mutations in the K-Ras that exert a strong pro-tumorigenic effect by activating several downstream effector pathways, including phosphatidylinositol-3-kinase (PI3K)-Akt. The role of frequently activated PI3K/Akt pathway in promoting PDAC aggressiveness is well established. Therapeutic approaches targeting PI3K and downstream signaling components in different cellular compartments, including tumor, stromal and immune cells, have directly impacted the tumor burden in this cancer type. Our previous work has demonstrated that targeting the PI3K/Akt/mTOR pathway reduced tumor growth and improved survival in the genetic mouse model of PDAC. Here, we discuss the significance of targeting PI3K signaling and the biological impact of PI3K inhibition in modulating the tumor-stromal immune crosstalk within the microenvironment of pancreatic cancer. Furthermore, this review updates on the current challenges involving the therapeutic implications of targeting this pathway in PDAC.

The development of food-borne and infectious diseases has increased globally at an anomalous rate and is combined with emerging social and economic problems. This highlights the need for new and improved antibacterial agents with novel and different mechanisms of action at regular intervals. Some chemical or artificial food additives are considered harmful if they are used beyond their permissible levels. Today, consumers are demanding alternative, green, safer, and natural food additives to increase the shelf life of food. Essential oils (EOs) are concentrated liquid mixtures of volatile compounds with antioxidant and antibacterial properties that can be used as natural, eco-friendly, renewable, and cost-effective additives. The use of combinations of different EOs and their components is a promising strategy to increase the synergistic and additive effects of EOs in foods. In this article, we review the recent literature on EOs concerning the chemical constituents, extraction methods, antioxidant and antibacterial activities, and their mechanisms of action. Additionally, we discuss the synergistic interaction of different EOs and their components, challenges, and future directions of EOs as natural food preservatives, with special emphasis on shelf life extension and applications in the packaging of food products.

Aqueous zinc-polymer batteries (AZPBs) comprising abundant Zn metal anode and redox-active polymer (RAP) cathodes can be a promising solution for accomplishing viable, safe and sustainable energy storage systems. Though a limited number of RAPs have been successfully applied as organic cathodes in AZPBs, their macromolecular engineering towards improving electrochemical performance is rarely considered. In this study, we systematically compare performance of AZPB comprising Zn metal anode and either poly(catechol) homopolymer (named P(4VC)) or poly(catechol) copolymer (named P(4VC 86 - stat -SS 14 )) as polymer cathodes. Sulfonate anionic pendants in copolymer not only rendered lower activation energy and higher rate constant, but also conferred lower charge-transfer resistance, as well as facilitated Zn 2+ mobility and less diffusion-controlled current responses compared to its homopolymer analogue. Consequently, the Zn||P(4VC 86 - stat -SS 14 ) full-cell exhibits enhanced gravimetric (180 versus 120 mAh g -1 at 30 mg cm -2 ) and areal capacity (5.4 versus 3.6 mAh cm -2 at 30 mg cm -2 ) values, as well as superior rate capability both at room temperature (149 versus 105 mAh g -1 at 150 C) and at -35 °C (101 versus 35 mAh g -1 at 30 C) compared to Zn||P(4VC) 100 . This overall improved performance for Zn||P(4VC 86 - stat -SS 14 ) is highly encouraging from the perspective applying macromolecular engineering strategies and paves the way for the design of advanced high-performance metal-organic batteries.

Detection of the temporal reversibility of a given process is an interesting time series analysis scheme that enables the useful characterisation of processes and offers an insight into the underlying processes generating the time series. Reversibility detection measures have been widely employed in the study of ecological, epidemiological and physiological time series. Further, the time reversal of given data provides a promising tool for analysis of causality measures as well as studying the causal properties of processes. In this work, the recently proposed Compression-Complexity Causality (CCC) measure (by the authors) is shown to be free of the assumption that the "cause precedes the effect", making it a promising tool for causal analysis of reversible processes. CCC is a data-driven interventional measure of causality (second rung on the Ladder of Causation ) that is based on Effort-to-Compress (ETC) , a well-established robust method to characterize the complexity of time series for analysis and classification. For the detection of the temporal reversibility of processes, we propose a novel measure called the Compressive Potential based Asymmetry Measure . This asymmetry measure compares the probability of the occurrence of patterns at different scales between the forward-time and time-reversed process using ETC. We test the performance of the measure on a number of simulated processes and demonstrate its effectiveness in determining the asymmetry of real-world time series of sunspot numbers, digits of the transcedental number π and heart interbeat interval variability.

Recently, infectious diseases caused by bacterial pathogens have become a major cause of morbidity and mortality globally due to their resistance to multiple antibiotics. This has triggered initiatives to develop novel, alternative antimicrobial materials, which solve the issue of infection with multidrug-resistant bacteria. Nanotechnology using nanoscale materials, especially multimetallic nanoparticles (NPs), has attracted interest because of the favorable physicochemical properties of these materials, including antibacterial properties and excellent biocompatibility. Multimetallic NPs, particularly those formed by more than two metals, exhibit rich electronic, optical, and magnetic properties. Multimetallic NP properties, including size and shape, zeta potential, and large surface area, facilitate their efficient interaction with bacterial cell membranes, thereby inducing disruption, reactive oxygen species production, protein dysfunction, DNA damage, and killing potentiated by the host's immune system. In this review, we summarize research progress on the synergistic effect of multimetallic NPs as alternative antimicrobial agents for treating severe bacterial infections. We highlight recent promising innovations of multimetallic NPs that help overcome antimicrobial resistance. These include insights into their properties, mode of action, the development of synthetic methods, and combinatorial therapies using bi- and trimetallic NPs with other existing antimicrobial agents.

Many neurological diseases and delineating pathological regions have been analyzed, and the anatomical structure of the brain researched with the aid of magnetic resonance imaging (MRI). It is important to identify patients with Alzheimer's disease (AD) early so that preventative measures can be taken. A detailed analysis of the tissue structures from segmented MRI leads to a more accurate classification of specific brain disorders. Several segmentation methods to diagnose AD have been proposed with varying complexity. Segmentation of the brain structure and classification of AD using deep learning approaches has gained attention as it can provide effective results over a large set of data. Hence, deep learning methods are now preferred over state-of-the-art machine learning methods. We aim to provide an outline of current deep learning-based segmentation approaches for the quantitative analysis of brain MRI for the diagnosis of AD. Here, we report how convolutional neural network architectures are used to analyze the anatomical brain structure and diagnose AD, discuss how brain MRI segmentation improves AD classification, describe the state-of-the-art approaches, and summarize their results using publicly available datasets. Finally, we provide insight into current issues and discuss possible future research directions in building a computer-aided diagnostic system for AD., Competing Interests: The authors declare no conflict of interest.

Over the past few decades, many pathogenic bacteria have become resistant to existing antibiotics, which has become a threat to infectious disease control worldwide. Hence, there has been an extensive search for new, efficient, and alternative sources of antimicrobial agents to combat multidrug-resistant pathogenic microorganisms. Numerous studies have reported the potential of both essential oils and metal/metal oxide nanocomposites with broad spectra of bioactivities including antioxidant, anticancer, and antimicrobial attributes. However, only monometallic nanoparticles combined with essential oils have been reported on so far with limited data. Bi- and tri-metallic nanoparticles have attracted immense attention because of their diverse sizes, shapes, high surface-to-volume ratios, activities, physical and chemical stability, and greater degree of selectivity. Combination therapy is currently blooming and represents a potential area that requires greater attention and is worthy of future investigations. This review summarizes the synergistic effects of essential oils with other antimicrobial combinations such as mono-, bi-, and tri-metallic nanocomposites. Thus, the various aspects of this comprehensive review may prove useful in the development of new and alternative therapeutics against antibiotic resistant pathogens in the future.